Master of Science / Department of Industrial & Manufacturing Systems Engineering / Shing I. Chang / This research aims to quantify human walking patterns through depth cameras to (1) detect walking pattern changes of a person with and without a motion-restricting device or a walking aid, and to (2) identify distinct walking patterns from different persons of similar physical attributes. Microsoft Kinect™ devices, often used for video games, were used to provide and track coordinates of 25 different joints of people over time to form a human skeleton. Two main studies were conducted. The first study aims at deciding whether motion-restricted devices such as a knee brace, an ankle brace, or walking aids – walkers or canes affect a person’s walking pattern or not. This study collects gait data from ten healthy subjects consisting of five females and five males walking a 10-foot path multiple times with and without motion-restricting devices. Their walking patterns were recorded in a form of time series via two Microsoft Kinect™ devices through frontal and sagittal planes. Two types of statistics were generated for analytic purposes. The first type is gait parameters converted from Microsoft Kinect™ coordinates of six selected joints. Then Sample Entropy (SE) measures were computed from the gait parameter values over time. The second method, on the other hand, applies the SE computations directly on the raw data derived from Microsoft Kinect™ devices in terms of (X, Y, Z) coordinates of 15 selected joints over time. The SE values were then used to compare the changes in each joint with and without motion-restricting devices. The experimental results show that both types of statistics are capable of detecting differences in walking patterns with and without motion-restricting devices for all ten subjects. The second study focuses on distinguishing two healthy persons with similar physical conditions. SE values from three gait parameters were used to distinguish one person from another via their walking patterns. The experimental results show that the proposed method using a star glyph summarizing the shape produced by the gait parameters is capable of distinguishing these two persons. Then multiple machine learning (ML) models were applied to the SE datasets from ten college-age subjects - five males and five females. In particular, ML models were applied to classify subjects into two categories: normal walking and abnormal walking (i.e. with motion-restricting devices). The best ML model (K-nearest neighborhood) was able to predict 97.3% accuracy using 10-fold cross-validation. Finally, ML models were applied to classify five gait conditions: walking normally, walking while wearing the ankle brace, walking while wearing the ACL brace, walking while using a cane, and walking while using a walker. The best ML model was again the K-nearest neighborhood performing at 98.7% accuracy rate.

The postural control-cognition dual-task literature has demonstrated greater postural stability through the examination of multiple kinetic and kinematic measures. Recently, sample entropy (SampEn) and wavelet discrete transform have supported the claim of automaticity, as higher SampEn values and a shift toward higher contribution from automatic sensory systems have been demonstrated in dual-task settings. In order to understand the cortical component of postural control, functional near-infrared spectroscopy (fNIRS) has been used to identify cortical activation under postural control conditions. However, the neural correlates of automatic postural behaviour have yet to be fully investigated. Therefore, the purpose of this study is to confirm the presence of automatic postural control through static and dynamic measurements, and to investigate the prefrontal cortex activation (PFC) when concurrently performing quiet standing and auditory cognitive tasks. Eighteen healthy young adults (21,4 ± 3,96yo), 12 females and 6 males, with no balance deficits were recruited. Participants were instructed to either quietly stand on a force platform (SM), perform three cognitive tasks while seated (SC) and perform a combination of SM and SC concurrently (DT). Results revealed automatic postural control as evidenced by lower area and standard deviation of center-of-pressure in DT conditions. As for SampEn and the wavelet analysis, greater values and a shift from vision to the cerebellum contribution were demonstrated in DT conditions. For the DNS task, a trend toward significantly lower right hemisphere PFC activation compared to left hemisphere activation in DT was evidenced, which potentially reiterate the presence of automaticity. Therefore, as demonstrated by this experiment, the simultaneous performance of a difficult cognitive task and posture yields automatic postural behaviour, and provides insight into the neural correlates of automaticity.

Prolonged performance of repetitive movements can lead to muscle fatigue, negatively impacting human performance. As a result, researchers have explored methods to effectively assess and quantify this phenomenon, where surface electromyography (sEMG) is a popular method to reveal information regarding muscle contractions. The continuous wavelet transform (CWT) captures the instantaneous frequency components of signals, which make it suitable for sEMG analyses of dynamic muscle contractions. Moreover, sample entropy (SampEn) can be used to quantify the complexity of the sEMG signal, which provides novel insights for assessing muscle fatigue. However, the amount of research on sEMG complexity analyses to assess muscle fatigue during dynamic contractions is limited. Therefore, the goal of this work was to: 1) calculate and compare the major frequency components (MFC) from CWT and modified SampEn (MSE) of sEMG signals during a repetitive trunk flexion-extension (F-E) task; and 2) determine which sEMG metric is more closely related to ground truth fatigue indicators including the visual analogue scale (VAS), maximum pulling force, and kinematic variability of movements. Seven male and five female participants performed up to twelve sets of 50 repetitive trunk FE movements based on pre-defined stopping criteria. Their VAS and maximum pulling strength were measured immediately after each set. The MFC from CWT and the MSE values were calculated from both the left and the right lumbar erector spinae (LES) throughout the movements. Trunk dynamic kinematic variability of every set was quantified by the spine motion composite index (SMCI). Repeated measures correlation coefficients (r) were used to calculate the relationship between MFC and MSE, as well as between these outcome variables and VAS, maximum pulling force, and SMCI across all participants. Visual inspection revealed that on average that both the MFC and the MSE of sEMG signals decreased as the fatiguing protocol progressed, where a significant correlation was found between the two sEMG metrics (r = 0.270, p = 0.006). No significant correlations were found between the two sEMG measures and the maximum pulling strength (r_MFC = 0.101, p = 0.313; r_MSE = 0.193, p = 0.051). Nevertheless, both sEMG metrics showed significant correlations with fatigue VAS, with the MFC having stronger correlations across all the participants (r_MFC = −0.602, p < 0.001) than the MSE (r_MSE = −0.248, p = 0.011). Significant negative correlations were also observed between the SMCI and both sEMG MFC (r_MFC = −0.268, p = 0.010) and MSE (r_MSE = −0.335, p = 0.001). Both sEMG metrics mapped onto the perceived fatigue and movement pattern variations during the task, suggesting they could be used for assessing fatigue during dynamic movements. However, the MFC had a stronger correlation with participants' perceived fatigue whereas MSE was more strongly correlated with kinematic variability. Continued research is required to further examine these relationships, as well as determine the best method of assessing changes in force output with muscle fatigue.

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A "complex" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T11:52:38Z No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 13129069 bytes, checksum: b427ff42ec7918c3d0cf7f63798ed648 (MD5) / Made available in DSpace on 2016-07-07T11:52:38Z (GMT). No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 13129069 bytes, checksum: b427ff42ec7918c3d0cf7f63798ed648 (MD5) Previous issue date: 2014-09-19 / The objective of this study was to uncloak the dynamic of climate of Brazil, seeking to measure the regularity and the long range autocorrelation of daily climate series of temperature of air (average, maximum, minimum, and temperature range), relative humidity of air average and wind speed average. The data were obtained by Instituto Nacional de Meteorologia (INMET), at 264 meteorological stations, in the period from January 1990 to December 2012. We use the Detrended Fluctuation Analysis to realize the estimation of the Hurst exponent, the Multiscale Sample Entropy to estimating the entropy of series and the Kriging to interpolate the estimates made. We observed that higher latitudes tend to attenuate the mean of temperatures of air maximum, minimum and average, but increase the variability of the same. This inversion of the magnitudes of the mean and standard deviation is also observed in the relative humidity of air. The means of the estimated Hurst exponents estimated for Brazil were 0.81, 0.79, 0.81, 0.77, 0.83 and 0.64, and the estimated Sample Entropy, 1.39, 1.78, 1.46, 1.41, 1.56 and 1.66, respectively for average, maximum and minimum temperatures of air, temperature range, relative humidity of air average and wind speed average. The values of the estimated Hurst exponents showed a positive correlation with latitude in the temperature variables studied. Such a correlation was not observed in other variables. This a correlation was not observed in other variables. The regularities of climate series in Brazil were medians. Spatially, the greatest changes occurred in estimates of entropies in the scale 1 to 2 of , in the Multiscale Sample Entropy. As from ≥2 the changes observed were more subtle. We observe the influence of the Equatorial Continental air mass in entropy of temperatures daily average and maximum of air. The climatic factor of altitude influenced with more frequently in the observed results, mainly on temperature variables. In some cases, the continentality and the air masses were also identified as important factors in characterizing the spatial distribution of estimates made. / O objetivo deste estudo foi desvendar a dinâmica climática do Brasil, buscando mensurar a regularidade e a autocorrelação de longo alcance em séries climáticas diárias de temperatura do ar (média, máxima, mínima, e amplitude térmica), umidade relativa média do ar e velocidade média diária do vento. Os dados foram obtidos pelo Instituto Nacional de Meteorologia, em 264 estações meteorológicas, no período de janeiro de 1990 a dezembro de 2012. Utilizamos o Detrended Fluctuation Analysis para realizar a estimativa do expoente de Hurst, o Multiscale Sample Entropy para as estimativas da entropia das séries e o Kriging para a interpolação das estimativas realizadas. Observamos que maiores latitudes tendem a atenuar as médias das temperaturas máxima, mínima e média do ar, porém aumentam a variabilidade das mesmas. Esta inversão entre as magnitudes da média e do desvio padrão também é observado na umidade relativa média do ar. As médias dos expoentes de Hurst estimados para todo o Brasil foram 0,81; 0,79; 0,81; 0,77; 0,83 e 0,64; e do Sample Entropy estimado, 1,39; 1,78; 1,46; 1,41; 1,56 e 1,66, respectivamente para séries diárias de temperatura média, máxima e mínima do ar, amplitude térmica do ar, umidade relativa média do ar e velocidade média do vento. Os valores do expoentes de Hurst estimados apresentaram uma correlação positiva com a latitude nas variáveis de temperatura do ar estudadas. Tal correlação não foi observada nas demais variáveis. As regularidades das séries climáticas no Brasil foram medianas. Espacialmente, as maiores alterações nas estimativas das entropias ocorreram na escala 1 para a 2 de , no Multiscale Sample Entropy. A partir de ≥2 as mudanças observadas foram mais sutis. Observamos influência da massa de ar Equatorial Continental na entropia das temperaturas do ar média e máxima diárias. O fator climático da altitude atuou com maior frequência sob os resultados observados, principalmente nas variáveis de temperatura. Em alguns casos, a continentalidade e as massas de ar também foram apontados como fatores importantes na caracterização da distribuição espacial das estimativas realizadas.

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Many research studies have analyzed the effect that whole-body vibration (WBV) has on sleep, and some have sought to use vibration to treat sleep disorders such as insomnia. It has been shown that low frequencies (f < 2Hz) are generally sleep inducing, but oscillations of this frequency are typically difficult to achieve using electromagnetic vibration drives. In the research that has been performed, optimal vibration parameters have not been determined, and the effects of multiple vibration sources vibrating at different frequencies to induce a low frequency traveling wave have not been explored. Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity was conducted to determine the effects of beat frequency vibration, and traditional standing wave vibration on sleep latency and quality. Participants were monitored using high-density electroencephalography (HD-EEG). Sleep latency was compared between treatment conditions. Trends of a decrease in sleep latency due to beat frequency vibration were found (p ≤ 0.181 for AASM latency, and p ≤ 0.068 for unequivocal sleep latency). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). Lower MSE was found in the transition from wake to N1 stage sleep but did not reach significance (p ≤ 0.300). During N2 sleep, beat frequency vibration shows lower MSE than the control session in the left frontoparietal region. This indicates that beat frequency vibration may lead to a decrease of conscious awareness during deeper stages of sleep. Standing wave vibration caused reduced Alpha activity and increased Delta activity during wake. Beat frequency vibration caused increased Delta activity during N2 sleep. These preliminary results suggest that beat frequency vibration may help individuals with insomnia symptoms by decreasing sleep latency, by reducing their conscious awareness, and by increasing sleep drive expression during deeper stages of sleep. Standing wave vibration may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that a dynamic vibration treatment may be beneficial. The application of vibration treatment as part of a heuristic sleep model is discussed.